Scientific direction Development of key enabling technologies
Transfer of knowledge to industry

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Study of EBIC and cathodoluminescence applied to narrow gap photodiodes for cooled IR détection

Département d'Optronique (LETI)

Laboratoire d'Imagerie IR



Since 40 years, CEA LETI is developing IR detection technologies using narrow gap semi-conductors. This led to the creation of the Lynred Company, formerly Known as Sofradir, now leader in the IR imaging market. In the frame of the collaborative work we have with Lynred for the development of new generations of IR imager, new characterisation needs appear. It addresses different issues, starting from the fine understanding of the photodiode operation when reducing its pixel pitch. It also addresses the understanding of the effects of the metallurgical and technological induced defects on the final IR detection performances. The proposal here is to study the behaviour of narrow gap IR photodiodes when excited with an electron beam within a scanning electron microscope (SEM). A mapping of the electron beam induced current (EBIC) brings important information about charge transport in the narrow gap, whereas the mapping of the associated induced IR luminescence (cathodo-luminescence) carries further and complementary information about radiative recombination of injected charges. This information is particularly interesting when interaction with defects occurs in the structure. In our characterisation group, the EBIC experiment is now operational at cryogenic temperatures. On the other hand, the cathodo-luminescence part of the experiment has to be developed to complete EBIC images. Once operational, the full picture EBIC+cathodo should be investigated using different samples from our fabrication line, focusing on small pixel pitches and high operation temperature structures, making the connection with all the other electro-optical characterisation benches available in our lab.

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Multimodal characterization of therapeutic phages

Département Microtechnologies pour la Biologie et la Santé (LETI)

Laboratoire Chimie, Capteurs et Biomatériaux



Health and environment technologies, medical devices (.pdf)

The rapid inexorable spread of antibiotic resistance is one of the critical challenges in health care for the coming decade. Patients increasingly encounter dead-ends, with no effective molecule. The quest for alternatives to antibiotic therapy is a major public health issue and should, according to the WHO, be given priority status. Phage therapy uses viruses known as bacteriophages, or ?phages? for short, that specifically infect and destroy bacteria without impact on human cells. They have been used for decades in some countries in Eastern Europe, but preparations from these countries cannot be imported in France or Western European countries as they fail to meet standard drug agency criteria (ANSM, EMEA). Some new techniques have to be developed and optimized for a better and faster characterization of these therapeutic viruses, during their amplification and purification, as well as during their storage or juste before dispensing medication. The study deals with innovative approaches based on microelectronics and nanotechnologies for rapid in-vitro quantification and characterization of therapeutic phages to facilitate selection of phages and QCs of phage therapeutic products: (i) a lensless imaging technique for fast phage titration based on the monitoring of lysis plaques (from 20-µm- to millimeter-size) over a wide field-of-view (up to at least 864 mm2), suited to continuous detection of phage plaque growth, (ii) a microsystem called SNR (Suspended Nanochannel Resonator) for phage purity assessment without culture/replication requirement based on rapid mass measurement of individual virions.

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Scalable and Precise Static Analysis of Memory for Low-Level Languages

Département Ingénierie Logiciels et Systèmes (LIST)

Laboratoire pour la Sûreté du Logiciel



Artificial intelligence & Data intelligence (.pdf)

The goal of the thesis is to develop an automated static analysis (based on abstract interpretation) to verify, in large code base in low-level compiled languages (e.g. C, C++, assembly, Rust, Fortran), security properties that are related to memory, lke flow information properties and absence of memory corruption. This problem has many applications in cybersecurity, as most of the software-related cybersecurity issues, and those that have the highest severity, come from memory safety errors (e.g. (buffer overflows, use-after-free, null pointer dereferences, wrong type punning, wrong interfacing between several languages, etc). The three main issues when designing such an automated static analysis is to keep the verification effort low, to handle large and complex systems, and to be precise enough so that the analysis does not report a large amount of false alarms. The privileged approach in this thesis will draw on the success of a new method using abstract domains parameterized by type invariants, which found a sweet spot between precision (i.e. few false alarms), efficiency (in computing resources), and required effort (by the user). This method allowed in particular to fully automatically prove absence of privilege escalation and of memory corruptions of an existing industrial microkernel from its machine code, using only 58 lines of annotations. Many research questions remain, and we will explore how to extend the analyzer to improve scalability (using compositional analysis), how to improve its expressivity (to show complex security properties like non-interference), how to improve precision without degrading efficiency, or how to further reduce the amount of annotations (using automatic inference of more precise type invariants).

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Trajectory Prediction for Autonomous Navigation

Département Systèmes et Circuits Intégrés Numériques

Laboratoire Intelligence Intégrée Multi-capteurs



Cyber physical systems - sensors and actuators (.pdf)

With the growing interest in Autonomous Vehicles (AV), perception systems play a central role in their navigation, with active developments from the research and automotive industry communities. Perception systems provide AVs with information about the driving situation. Basically, advanced algorithms model the vehicle environment using a map by processing past and present data from on-board sensors such as cameras, LiDARs, radars and ultrasounds. The future evolution of the driving environment is predicted in order to plan safe trajectory, avoid collisions and make navigational decisions. CEA has developed a patented on-board sensor fusion technology that exploits the occupancy grid paradigm to model the vehicle environment. This grid provides a probabilistic estimate of occupied and free regions. The estimation of obstacle movement is also under development. However, a prediction layer that estimates the likely future trajectories of moving obstacles is still missing. The objective of the PhD thesis is to develop an embedded trajectory prediction algorithm for autonomous navigation. Trajectory prediction is a spatio-temporal (4D) problem where uncertainty is essential to evaluate the probable short-term evolution of a driving scenario. The diversity of moving obstacles makes trajectory prediction very difficult when integrated within lightweight computing platforms. In fact, a moving car does not have the same degree of freedom as a pedestrian. Prediction models can take into account the nature of moving obstacles if this information is available (for example, provided by artificial intelligence). Otherwise, prediction models must adapt to the available data. During the thesis, the PhD student will first focus on the probabilistic modeling of motion and trajectory. Then, he/she will propose a low-complexity algorithmic solution that can run in real time on an embedded computing platform. The PhD student will be hosted in a team whose expertise is the development of advanced and lightweight perception solutions that can be integrated into embedded systems. The PhD student will collaborate with researchers, engineers and other PhD students from various scientific fields. The candidate must have a strong mathematical background in probability/statistics, computer science and software prototyping (matlab/python, C++). Knowledge and skills in artificial intelligence and data fusion will be a plus.

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